EP3641349B1 - Procédé, système et programmes informatiques permettant de fournir un modèle utilisateur informé de la localisation pour préserver la confidentialité de l'utilisateur - Google Patents

Procédé, système et programmes informatiques permettant de fournir un modèle utilisateur informé de la localisation pour préserver la confidentialité de l'utilisateur Download PDF

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Publication number
EP3641349B1
EP3641349B1 EP18382740.1A EP18382740A EP3641349B1 EP 3641349 B1 EP3641349 B1 EP 3641349B1 EP 18382740 A EP18382740 A EP 18382740A EP 3641349 B1 EP3641349 B1 EP 3641349B1
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EP
European Patent Office
Prior art keywords
user
data
computing device
heatspot
heatspots
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Application number
EP18382740.1A
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German (de)
English (en)
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EP3641349A1 (fr
Inventor
Johan LANTZ
Aleksandar Matic
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Koa Health BV
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Koa Health BV
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Priority to EP18382740.1A priority Critical patent/EP3641349B1/fr
Priority to ES18382740T priority patent/ES2911073T3/es
Priority to PCT/EP2019/078075 priority patent/WO2020079075A1/fr
Publication of EP3641349A1 publication Critical patent/EP3641349A1/fr
Priority to US17/232,127 priority patent/US20210235261A1/en
Application granted granted Critical
Publication of EP3641349B1 publication Critical patent/EP3641349B1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/2866—Architectures; Arrangements
    • H04L67/30—Profiles
    • H04L67/306—User profiles
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/535—Tracking the activity of the user
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
    • H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/60—Context-dependent security
    • H04W12/63—Location-dependent; Proximity-dependent
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/2111—Location-sensitive, e.g. geographical location, GPS
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/2151—Time stamp
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2209/00—Additional information or applications relating to cryptographic mechanisms or cryptographic arrangements for secret or secure communication H04L9/00
    • H04L2209/16—Obfuscation or hiding, e.g. involving white box
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2209/00—Additional information or applications relating to cryptographic mechanisms or cryptographic arrangements for secret or secure communication H04L9/00
    • H04L2209/80—Wireless

Definitions

  • This invention relates to a method, and corresponding system and computer programs, for ensuring user privacy for sensor data collected from a mobile computing device such as a smartphone.
  • APPS that take the advantage of phone sensors to deliver or improve their services, thus they often rely on privacy sensitive data.
  • One common feature is geofencing where APPS can interact with the physical world to improve engagement and timeliness of interaction with a user.
  • New techniques/solutions are therefore needed to process personal information in a more anonymous way, so the information can be shared with backend services capable of building advanced user models and apply machine learning algorithms but without the risk of exposing information that could uniquely identify the user(s).
  • the invention is defined by independent method claim 1, independent system claim 6 and independent non-transitory computer-readable medium claim 7. Further details are defined in the dependent claims.
  • the heatspots include different areas of different significance for the user.
  • the heatspots have a given radius, equal or different among them, that can range from a few meters to several kilometers.
  • the sensor capture module may reside in the platform layer of the application meaning that there is a separate version for iOS TM and Android TM . Nonetheless, the concept is not limited to a specific platform and similar features could be available on other mobile platforms, embedded systems (loT) or even web browsers.
  • the processing of the collected sensor data further comprises providing at least one timestamp to each heatspot indicating the moment of time the user reached the heatspot.
  • each unique identifier is encrypted based at least on a part of the location coordinates of the predetermined area.
  • present invention allows for reaching an optimal trade-off between the user modeling power and the level of data sensitivity. Moreover, present invention increases user trust and decreases risk in case of data breaches. Besides, higher compliance with data regulations is achieved.
  • Present invention focuses on privacy preservation while still allowing for the sensor data collection and user modelling.
  • the descriptions below focus on sensor data that can potentially expose private information and how that can be anonymized without losing the ability to process the data in a personalized way.
  • the aim of the present invention is to build a good user model that can be 100% anonymous using data that anonymized while still being equally or close to equally relevant as its non-privacy invasive counterpart.
  • the heatspot works in such a way that for each location obtained from the user's mobile computing device it compares it to a list of locally cached areas within a certain radius and if there is a previous match the number of "hits" in that area is increased. The benefit of this is that it does not require continuous monitoring; on the contrary by obtaining a location at regular or fairly regular intervals the reliability of the heatspot importance is improved.
  • Heatspot 1 as User A's Home
  • Heatspot 2 as User A's workplace
  • Heatspots 3-5 intermediate points such as commuting.
  • the granularity is further improved since it will allow transition monitoring between heatspots allowing user flows to be simulated without exposing location details.
  • the user can be in control of this. This is accomplished by exposing an option on application or user level that controls the size of the heatspot.
  • the heatspot radius must be relatively low to be able to determine if the user is at home or in another heatspot.
  • it might be sufficient to have a larger heatspot radius for instance if it needs to be detected if the user is travelling for work or spend weekends away without exposing to where, then a heatspot the size of a city would be more than sufficient. In both cases the exact location is never compromised but having the option to tune the granularity might offer more peace of mind for the user.
  • the heatspot is simply labeled or identified with an identifier that is specific for each user, i.e. users A, B and C will all have Heatspots 0, 1, 2 and so on.
  • the identifier is further encrypted based at least on a part of the location coordinates of the predetermined area.
  • the computer would be able to correlate behaviors, movements etc. between users active in the same heatspots. It could also be used to conclude if users spending a lot of time in similar areas also share similar behaviors, problems etc.
  • the computer randomly creates a seed for creating a salt key. Then, the computer automatically creates the random salt key (with a pre-defined number of characters), encrypts it and stores it for the future use. The computer also decides on a hashing technique to be used to obfuscate the locations, e.g.
  • SHA-256 note that the computer can change a hashing technique over time to use the latest one
  • the mobile computing device applies the hashing technique with the salt key to every location and sends only a hash to the computer.
  • the application comprises several parts: an interactive therapy program designed to address the symptoms, a chat with the therapist or an anonymous support group, and other features. Now while the user may follow the program at certain pace or interact with the therapist or support group on random occasions, these are all user initiated actions. There is also a need for preventive measures and detecting anomalies in the user movement patterns as a good indicator that something might be wrong.
  • the application can query the user about the current perceived health state, then recommend the user to take a walk and finally "alert" the peers about a potentially unhealthy situation. In no case would this expose the user's exact whereabouts.
  • the APP can provide a service for detecting early signs that a user is going to experience a mental health crisis (such as depression, mania, or similar).
  • a mental health crisis such as depression, mania, or similar.
  • the literature showed that mobility patterns are important predictors of the upcoming crises.
  • using raw locations is considered to be extremely privacy invasive, and in particular patients do not feel comfortable to share it.
  • storing raw locations poses additional requirements, for instance GDRP imposes "high" security measures that are extremely challenging to comply to particularly for smaller companies (such as physical security, logging not only electronic access to the server but authenticating people who are in physical vicinity of the server and granting special permissions, etc.).
  • Storing heatspots instead of raw locations eliminates the data security requirements, while still allowing for the models to incorporate the analysis of mobility patterns (e.g. knowing that a sequence of very specific locations is a predictor of the crisis, the algorithm can have the same accuracy with heatspots like in the case of raw locations).
  • an APP delivers notifications to its users
  • the right timing is crucial for the engagement. Knowing in which heatspots its users are more responsive for specific time periods, the "right time” algorithm can work therefore without the need to store real locations.
  • the very same concept of heatspots applies for the obfuscation of the internet logs, representing online whereabouts as opposed to locations that represent whereabouts in real life.
  • the granularity is defined in the following way (note that the granularity was defined based on distances in the location use case).
  • Each next visibility level has one degree of a lower granularity from the previous one.
  • the above list is ordered from the lowest to the highest granularity with respect to the heatspot concept.
  • variations in the above categories are allowed as long as they provide different levels of the URL visibility with the related partial or full obfuscation.
  • the Bluetooth sensor is responsible for scanning the surroundings for Bluetooth or Bluetooth LE devices. This provides a way to detect which beacons that normally is available in the surroundings of the user.
  • the most obvious example is a Bluetooth smartphone that would identify another individual but other devices such as smart speakers, TV's etc. could indicate incoming level and other interesting parameters that are valuable for the user modelling.
  • Bluetooth identifier can indirectly reveal extremely privacy sensitive information, e.g. which exactly device a user is the surrounding of at 2am during the weekends. It could however still be valuable for the model to know that this device is frequently or repeatedly present in the surroundings of the user. If used in a raw format, it is possible to reverse engineer if the identifier corresponds to a mobile phone (therefore a person) or to a specific device, TV, headphones, laptop, etc.
  • Bluetooth address should not be shared with the backend for analysis, unless protected.
  • the general flow from device detection to recommendation via analysis is described on Fig. 4 .
  • Each APP can generate a unique and persistent identifier id. This id will be used to hash/encrypt the remote Bluetooth device address. For example, Bluetooth address: AABBCCDDEEFF11 would be 45fe12aa673423. This means that even if User A and User B sees the same device, they will report different identifiers to the computer/server. Recognition can only be done for the same reporting device (seeing the same beacon twice will generate the same result).
  • Fig. 5 illustrates an example of how the same device generates two different identifiers when reported to the server.
  • Bluetooth address AABBCCDDEEFF11 would be AABBCCaa673423 (3 first bytes are preserved).
  • Fig. 6 illustrates an example in which User A and B reports User C to the server but only the manufacturer identifier is preserved.
  • User B has Bluetooth address: AABBCCDDEEFF11.
  • AABBCCaa673423 When User A sees user B he will report AABBCCaa673423 to the backend.
  • User C sees user B he will also report: AABBCCaa673423. This way it can be deducted that User A and User C both interact with User B even though the exact details of User B's address is not shared.
  • Fig. 7 is an illustration of how User A and User B would both report the same, anonymized identifier for User C.
  • the maximum Bluetooth range (for v5.0) is around 120m.
  • Fig. 8 illustrates how User B's privacy settings eliminate User A from the devices reported for analysis since it is outside of the predefined range.
  • the server in any of the described embodiments, will allow computing a model of how the user interacts with other peers and devices. It will also allow the system to learn about random encounters vs repeated ones. Devices that are part of the home scenario vs work. In an extension, it can also be used to anonymously map circles of users to each other if they are all using the same platform. In contrast to other commercial and ad focused services, this is all done while preserving the privacy of both the user and the detected peers.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Security & Cryptography (AREA)
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  • Computer Hardware Design (AREA)
  • Health & Medical Sciences (AREA)
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Claims (8)

  1. Procédé pour fournir un modèle d'utilisateur sensible à l'emplacement protégeant la vie privée de l'utilisateur, le procédé comprenant
    a) la collecte, par un module de capture de capteur, des données de capteur provenant d'une pluralité de capteurs installés sur un dispositif informatique mobile d'un utilisateur ;
    b) le traitement, par un ordinateur, desdites données de capteur collectées de manière anonyme en regroupant les données de capteur collectées en différents points chauds, lesdits points chauds incluant différentes zones d'importance différente pour ledit utilisateur, et chacun des points chauds comprenant un rayon donné ;
    c) l'étiquetage, par l'ordinateur, de chacun des points chauds avec un identifiant unique correspondant à une zone prédéterminée, et
    d) la génération, par l'ordinateur, d'un modèle d'utilisateur sensible à l'emplacement en fonction desdits identifiants, de sorte que ledit modèle d'utilisateur sensible à l'emplacement généré soit approprié pour offrir des recommandations à l'utilisateur par l'intermédiaire du dispositif informatique mobile, pour effectuer des études et/ou fournir une entrée à d'autres modèles d'utilisateur,
    dans lequel le procédé comprend en outre ;
    effectuer les étapes a)-d) pour une pluralité d'utilisateurs différents actifs dans les mêmes points chauds, de telle sorte qu'un modèle d'utilisateur sensible à l'emplacement soit généré pour chacun de la pluralité d'utilisateurs différents :
    le calcul, par l'ordinateur, d'un germe et l'utilisation dudit germe calculé pour créer et chiffrer automatiquement une clé de sel aléatoire ;
    la détermination, par l'ordinateur, d'une technique de hachage à utiliser pour masquer les différents points chauds, et transmettre au dispositif informatique mobile de chaque utilisateur de ladite pluralité d'utilisateurs différents la clé de sel aléatoire chiffrée et la technique de hachage déterminée ; et
    l'application, par chaque dispositif informatique mobile, de la technique de hachage avec la clé de sel à chaque point chaud et la transmission d'un hachage à l'ordinateur, et
    dans lequel les données de capteur collectées incluent un ou plusieurs des éléments suivants : des données d'accéléromètre ; des données d'activité ; des données sur les applications installées dans le dispositif informatique : des données sur un niveau de batterie du dispositif informatique ; des données sur les balises Bluetooth dans le point chaud ; des journaux d'appels ; des données sur le dispositif informatique, incluant le modèle et/ou le nom de la marque ; des données indiquant si un casque d'écoute est branché ou non : des journaux Internet et/ou historique de navigation ; le niveau de luminosité actuel, des données de localisation, si de la musique est en train d'être jouée ou non, le niveau de bruit ambiant ; des données de podomètre : des données de réseau concernant le dispositif informatique incluant l'itinérance, l'opérateur, la tour de téléphonie cellulaire, des données de transmission/réception, mobile/Wi-Fi, le mode avion et/ou le pays ; des données sur les lieux ou le type d'établissements à proximité du point chaud ; des données indiquant si un écran du dispositif informatique est allumé/éteint ; des journaux SMS ; des données indiquant les transitions d'activité de l'utilisateur ; et/ou des données indiquant la dynamique de marche de l'utilisateur.
  2. Procédé selon la revendication 1, dans lequel ladite étape b) comprend en outre la fourniture d'au moins un horodatage à chaque point chaud indiquant le moment temporel où l'utilisateur a atteint le point chaud.
  3. Procédé selon les revendications précédentes, dans lequel chaque identifiant unique est chiffré en fonction d'au moins une partie des coordonnées de localisation de la zone prédéterminée.
  4. Procédé selon les revendications précédentes, dans lequel les rayons des différents points chauds sont égaux ou différents entre eux.
  5. Procédé selon les revendications précédentes, dans lequel le rayon des différents points chauds varie de quelques mètres à plusieurs kilomètres.
  6. Système pour fournir un modèle d'utilisateur sensible à l'emplacement protégeant la vie privée de l'utilisateur, comprenant ;
    une pluralité de dispositifs informatiques mobiles, chacun appartenant à un utilisateur d'une pluralité d'utilisateurs différents ;
    un module de capture de capteur installé sur chaque dispositif informatique mobile de la pluralité de dispositifs informatiques mobiles pour collecter des données de capteur à partir d'une pluralité de capteurs installés sur chaque dispositif informatique mobile, des données de capteur collectées incluant un ou plusieurs des éléments suivants : des données d'accéléromètre ; des données d'activité ; des données sur les applications installées dans le dispositif informatique ; des données sur un niveau de batterie du dispositif informatique ; des données sur les balises Bluetooth dans le point chaud ; des journaux d'appels ; des données sur le dispositif informatique, incluant le modèle et/ou le nom de la marque ; des données indiquant si un casque d'écoute est branché ou non ; des journaux Internet et/ou historique de navigation, le niveau de luminosité actuel ; des données de localisation ; si de la musique est en train d'être jouée ou non ; le niveau de bruit ambiant ; des données de podomètre ; des données de réseau concernant le dispositif informatique incluant l'itinérance, l'opérateur, la tour de téléphonie cellulaire, des données de transmission/réception, mobile/Wi-Fi, le mode avion et/ou le pays ; des données sur les lieux ou le type d'établissements à proximité du point chaud ; des données indiquant si un écran du dispositif informatique est allumé/éteint ; des journaux SMS ; des données indiquant les transitions d'activité de l'utilisateur ; et/ou des données indiquant la dynamique de marche de l'utilisateur ;
    un ordinateur incluant un ou plusieurs processeurs et au moins une mémoire, dans lequel l'un ou plusieurs processeurs sont configurés pour :
    traiter les données de capteur collectées de chaque utilisateur de la pluralité d'utilisateurs différents de manière anonyme en regroupant les données de capteur collectées en différents points chauds, lesdits points chauds incluant différentes zones d'importance différente pour l'utilisateur, et chacun des points chauds comprenant un rayon donné :
    étiqueter chaque point chaud avec un identifiant unique correspondant à une zone prédéterminée ;
    générer un modèle d'utilisateur sensible à l'emplacement pour chaque utilisateur en fonction desdits identifiants, de sorte que ledit modèle d'utilisateur sensible à l'emplacement généré soit approprié pour offrir des recommandations à l'utilisateur par l'intermédiaire du dispositif informatique mobile, effectuer des études et/ou fournir une entré à d'autres modèles d'utilisateur :
    calculer une graine et utiliser la graine pour créer et chiffrer automatiquement une clé de sel aléatoire : et
    déterminer une technique de hachage à utiliser pour masquer les différents points chauds, et transmettre au dispositif informatique mobile de chaque utilisateur de la pluralité d'utilisateurs différents la clé de sel aléatoire chiffrée et la technique de hachage déterminée, dans lequel chaque dispositif informatique mobile est en outre configuré pour appliquer la technique de hachage avec la clé de sel à chaque point chaud et pour transmettre un hachage à l'ordinateur.
  7. Support lisible par ordinateur non transitoire, comprenant des instructions de code qui, lorsqu'elles sont exécutées par un processeur d'un ordinateur, mettent en œuvre un procédé pour fournir un modèle d'utilisateur sensible à l'emplacement protégeant la vie privée de l'utilisateur selon la revendication 1.
  8. Support lisible par ordinateur non-transitoire selon la revendication 7, dans lequel le processeur, lors du traitement des données de capteur collectées, fournit en outre au moins un horodatage à chaque point chaud indiquant le moment temporel où chaque utilisateur a atteint le point chaud.
EP18382740.1A 2018-10-17 2018-10-17 Procédé, système et programmes informatiques permettant de fournir un modèle utilisateur informé de la localisation pour préserver la confidentialité de l'utilisateur Active EP3641349B1 (fr)

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Application Number Priority Date Filing Date Title
EP18382740.1A EP3641349B1 (fr) 2018-10-17 2018-10-17 Procédé, système et programmes informatiques permettant de fournir un modèle utilisateur informé de la localisation pour préserver la confidentialité de l'utilisateur
ES18382740T ES2911073T3 (es) 2018-10-17 2018-10-17 Método, sistema y programas informáticos para proporcionar un modelo de usuario con conocimiento de ubicación que conserva la privacidad del usuario
PCT/EP2019/078075 WO2020079075A1 (fr) 2018-10-17 2019-10-16 Procédé, système et programmes informatiques permettant de fournir un modèle d'utilisateur sensible à l'emplacement protégeant la vie privée de l'utilisateur
US17/232,127 US20210235261A1 (en) 2018-10-17 2021-04-15 Location Aware User Model That Preserves User Privacy Of Sensor Data Collected By A Smartphone

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EP18382740.1A EP3641349B1 (fr) 2018-10-17 2018-10-17 Procédé, système et programmes informatiques permettant de fournir un modèle utilisateur informé de la localisation pour préserver la confidentialité de l'utilisateur

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EP3641349A1 EP3641349A1 (fr) 2020-04-22
EP3641349B1 true EP3641349B1 (fr) 2022-02-09

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